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Longevity risk and economic growth in sub-populations: evidence from Italy

Author

Listed:
  • Giuseppina Bozzo

    (Sapienza University of Rome)

  • Susanna Levantesi

    (Sapienza University of Rome)

  • Massimiliano Menzietti

    (University of Calabria)

Abstract

Forecasting mortality is still a big challenge for Governments that are interested in reliable projections for defining their economic policy at local and national level. The accuracy of mortality forecasting is considered an important issue for longevity risk management. In the literature, many authors have analyzed the long-run relationship between mortality evolution and socioeconomic variables, such as economic growth, unemployment rate or educational level. This paper investigates the existence of a link between mortality and real gross domestic product per capita (GDPPC) over time in the Italian regions. Empirical evidence shows the presence of a relationship between mortality and the level of real GDPPC (and not its trend). Therefore, we propose a multi-population model including the level of real GDPPC and we compare it with the Boonen–Li model (Boonen and Li in Demography 54:1921–1946, 2017). The validity of the model is tested in the out-of-sample forecasting experiment.

Suggested Citation

  • Giuseppina Bozzo & Susanna Levantesi & Massimiliano Menzietti, 2021. "Longevity risk and economic growth in sub-populations: evidence from Italy," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 101-115, June.
    • Handle: RePEc:spr:decfin:v:44:y:2021:i:1:d:10.1007_s10203-020-00275-x
    DOI: 10.1007/s10203-020-00275-x
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    References listed on IDEAS

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    1. Danesi, Ivan Luciano & Haberman, Steven & Millossovich, Pietro, 2015. "Forecasting mortality in subpopulations using Lee–Carter type models: A comparison," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 151-161.
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    Cited by:

    1. Francesca Perla & Salvatore Scognamiglio, 2023. "Locally-coherent multi-population mortality modelling via neural networks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 157-176, June.

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